Smart coordination of predictive load balancing for residential electric vehicles based on EMD-Bayesian optimised LSTM

dc.authorid0000-0001-9970-3248
dc.authorid0000-0002-4576-1941
dc.contributor.authorAkıl, Murat
dc.contributor.authorDokur, Emrah
dc.contributor.authorBayındır, Ramazan
dc.date.accessioned2022-10-03T11:01:29Z
dc.date.available2022-10-03T11:01:29Z
dc.date.issued2022
dc.departmentTeknik Bilimler Meslek Yüksekokulu
dc.description.abstractThe charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi-state and high-frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short-Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short-Term Memory is applied to forecast for each sub-component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data-driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model.
dc.identifier.doi10.1049/rpg2.12572
dc.identifier.endpage-en_US
dc.identifier.issn1752-1416
dc.identifier.issn1752-1424
dc.identifier.issue-en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage-en_US
dc.identifier.urihttps:/dx.doi.org/10.1049/rpg2.12572
dc.identifier.urihttps://hdl.handle.net/20.500.12451/9717
dc.identifier.volume-en_US
dc.identifier.wosWOS:000839441500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIET - Institution of Engineering and Technology
dc.relation.ispartofIET Renewable Power Generation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectEmpirical Mode Decomposition
dc.subjectPerformance Analysis
dc.subjectConsumption
dc.subjectmanagement
dc.subjectForecasts
dc.subjectDemand
dc.titleSmart coordination of predictive load balancing for residential electric vehicles based on EMD-Bayesian optimised LSTM
dc.typeArticle

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